Chess KingVideo software Tutorial #15 by Steve Lopez. You will learn how to print your game to PDF with diagrams and variations. Learn how to add a diagram to your notation. Chess King with Houdini 2 is an affordable and powerful chess software. With Chess King you can play chess, solve puzzles, analyze your games with the strongest engine available and have access to more than the 5 million game GigaKing database. At the moment there is a Coupon Code INTROKING50 that will let you save $50 from the list $99 price of Chess King on chess - king dot com (chess dash king dot com).

published:14 Feb 2012

views:1731

A look at ChessStudy: PDFPGN Pro app for android!
https://play.google.com/store/apps/details?id=org.savanteDroid.chessClass&pageId=104197216234285425581&authuser=4

1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinfor...
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinforcement_learning
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

Adobe Systems made the PDF specification available free of charge in 1993. PDF was a proprietary format controlled by Adobe, until it was officially released as an open standard on July 1, 2008, and published by the International Organization for Standardization as ISO 32000-1:2008, at which time control of the specification passed to an ISO Committee of volunteer industry experts. In 2008, Adobe published a Public Patent License to ISO 32000-1 granting royalty-free rights for all patents owned by Adobe that are necessary to make, use, sell, and distribute PDF compliant implementations. However, there are still some proprietary technologies defined only by Adobe, such as Adobe XML Forms Architecture and JavaScript for Acrobat, which are referenced by ISO 32000-1 as normative and indispensable for the application of the ISO 32000-1 specification. These proprietary technologies are not standardized and their specification is published only on Adobe’s website. The ISO committee is actively standardizing many of these as part of ISO 32000-2.

Chess

Chess is a two-player board game played on a chessboard, a checkered gameboard with 64squares arranged in an eight-by-eight grid. Chess is played by millions of people worldwide, both amateurs and professionals.

Each player begins the game with 16 pieces: one king, one queen, two rooks, two knights, two bishops, and eight pawns. Each of the six piece types moves differently. The most powerful piece is the queen and the least powerful piece is the pawn. The objective is to 'checkmate' the opponent's king by placing it under an inescapable threat of capture. To this end, a player's pieces are used to attack and capture the opponent's pieces, while supporting their own. In addition to checkmate, the game can be won by voluntary resignation by the opponent, which typically occurs when too much material is lost, or if checkmate appears unavoidable. A game may also result in a draw in several ways.

Chess is believed to have originated in India, some time before the 7th century; the Indian game of chaturanga is also the likely ancestor of xiangqi and shogi. The pieces took on their current powers in Spain in the late 15th century; the rules were finally standardized in the 19th century.

King (chess)

In chess, the king (♔, ♚) is the most important piece. The object of the game is to trap the opponent's king so that its escape is not possible (checkmate). If a player's king is threatened with capture, it is said to be in check, and the player must remove the threat of capture on the next move. If this cannot be done, the king is said to be in checkmate. Although the king is the most important piece, it is usually the weakest piece in the game until a later phase, the endgame.

Movement

White starts with the king on the first rank to the right of the queen. Black starts with the king directly across from the white king. With the squares labeled as in algebraic notation, the white king starts on e1 and the black king on e8.

A king can move one square in any direction (horizontally, vertically, or diagonally) unless the square is already occupied by a friendly piece or the move would place the king in check. As a result, the opposing kings may never occupy adjacent squares (see opposition), but the king can give discovered check by unmasking a bishop, rook, or queen. The king is also involved in the special move of castling.

In machine learning, the environment is typically formulated as a Markov decision process (MDP) as many reinforcement learning algorithms for this context utilize dynamic programming techniques. The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.

Turing machine

A Turing machine is an abstract machine that manipulates symbols on a strip of tape according to a table of rules; to be more exact, it is a mathematical model that defines such a device. Despite the model's simplicity, given any computer algorithm, a Turing machine can be constructed that is capable of simulating that algorithm's logic.

The machine operates on an infinite memory tape divided into cells. The machine positions its head over a cell and "reads" (scans) the symbol there. Then per the symbol and its present place in a finite table of user-specified instructions the machine (i) writes a symbol (e.g. a digit or a letter from a finite alphabet) in the cell (some models allowing symbol erasure and/or no writing), then (ii) either moves the tape one cell left or right (some models allow no motion, some models move the head), then (iii) (as determined by the observed symbol and the machine's place in the table) either proceeds to a subsequent instruction or halts the computation.

download Learn to Play Chess pdf

2:07

Tutorial #15: How to Print a Game to PDF in Chess King

Tutorial #15: How to Print a Game to PDF in Chess King

Tutorial #15: How to Print a Game to PDF in Chess King

Chess KingVideo software Tutorial #15 by Steve Lopez. You will learn how to print your game to PDF with diagrams and variations. Learn how to add a diagram to your notation. Chess King with Houdini 2 is an affordable and powerful chess software. With Chess King you can play chess, solve puzzles, analyze your games with the strongest engine available and have access to more than the 5 million game GigaKing database. At the moment there is a Coupon Code INTROKING50 that will let you save $50 from the list $99 price of Chess King on chess - king dot com (chess dash king dot com).

14:38

Studying chess books on Android

Studying chess books on Android

Studying chess books on Android

A look at ChessStudy: PDFPGN Pro app for android!
https://play.google.com/store/apps/details?id=org.savanteDroid.chessClass&pageId=104197216234285425581&authuser=4

1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinfor...
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinforcement_learning
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

0:17

Download Character Education with Chess pdf

Download Character Education with Chess pdf

Download Character Education with Chess pdf

2:48

Chess King 4 Tutorial 15 - Diagrams and Export to PDF in Chess King 4 for PC/Mac

Chess King 4 Tutorial 15 - Diagrams and Export to PDF in Chess King 4 for PC/Mac

Chess King 4 Tutorial 15 - Diagrams and Export to PDF in Chess King 4 for PC/Mac

Instructive game tags: AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns, Interesting material imbalance
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinforcement_learning
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

1:12

how to win chess in 7 moves

how to win chess in 7 moves

how to win chess in 7 moves

0:21

How Computers Play Chess PDF

How Computers Play Chess PDF

How Computers Play Chess PDF

0:20

Computer Chess Compendium PDF

Computer Chess Compendium PDF

Computer Chess Compendium PDF

0:16

Download New World Chess Champion All the Championship Games With Annotations Russian Chess Pdf

Download New World Chess Champion All the Championship Games With Annotations Russian Chess Pdf

Download New World Chess Champion All the Championship Games With Annotations Russian Chess Pdf

download Learn to Play Chess pdf

published: 13 Nov 2016

Tutorial #15: How to Print a Game to PDF in Chess King

Chess KingVideo software Tutorial #15 by Steve Lopez. You will learn how to print your game to PDF with diagrams and variations. Learn how to add a diagram to your notation. Chess King with Houdini 2 is an affordable and powerful chess software. With Chess King you can play chess, solve puzzles, analyze your games with the strongest engine available and have access to more than the 5 million game GigaKing database. At the moment there is a Coupon Code INTROKING50 that will let you save $50 from the list $99 price of Chess King on chess - king dot com (chess dash king dot com).

published: 14 Feb 2012

Studying chess books on Android

A look at ChessStudy: PDFPGN Pro app for android!
https://play.google.com/store/apps/details?id=org.savanteDroid.chessClass&pageId=104197216234285425581&authuser=4

Chess KingVideo software Tutorial #15 by Steve Lopez. You will learn how to print your game to PDF with diagrams and variations. Learn how to add a diagram to your notation. Chess King with Houdini 2 is an affordable and powerful chess software. With Chess King you can play chess, solve puzzles, analyze your games with the strongest engine available and have access to more than the 5 million game GigaKing database. At the moment there is a Coupon Code INTROKING50 that will let you save $50 from the list $99 price of Chess King on chess - king dot com (chess dash king dot com).

Chess KingVideo software Tutorial #15 by Steve Lopez. You will learn how to print your game to PDF with diagrams and variations. Learn how to add a diagram to your notation. Chess King with Houdini 2 is an affordable and powerful chess software. With Chess King you can play chess, solve puzzles, analyze your games with the strongest engine available and have access to more than the 5 million game GigaKing database. At the moment there is a Coupon Code INTROKING50 that will let you save $50 from the list $99 price of Chess King on chess - king dot com (chess dash king dot com).

1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinfor...
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinfor...
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinforcement_learning
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinforcement_learning
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

Instructive game tags: AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns, Interesting material imbalance
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinforcement_learning
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

Instructive game tags: AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns, Interesting material imbalance
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinforcement_learning
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

20/20 Chess Calculation

Chess video looking at the process of calculation and how it can be utilised to evaluate positions in a chess game.
1. We ascertain candidate moves from the most forcing moves in the position,
a) checks (the most forcing)
b) captures
c) attacking moves
2. we visualise the sequence of events and split this sequence into episodes if its very long
3. We evaluate the position after the sequence of events and ask ourselves important question
a) does it increase or decrease the mobility of my chess pieces?
does it increase or decrease the mobility of my opponents chess pieces
b) does my opponent have any forcing moves (checks, captures, attacking moves) after the sequence of events has transpired? If so, are they dangerous?
thus calculation is used to subject chess moves to falsificat...

Famous Chess Game: Kasparov vs Topalov 1999 (Kasparov's Immortal)

In what is arguably the greatest chess match ever played, Kasparov shows why he is considered to be the best chess player of all time in his "Immortal" game. There are so many amazing moves I lost count. Hopefully you learn as much from the game as I did studying it.
http://www.thechesswebsite.com
Chess Software used in the video can be found at http://www.chesscentral.com and http://www.chessok.com

Advanced Chess Strategy Part 1

Chess Openings - Colorado Gambit

The ColoradoGambit is an aggressive line in the Nimzowitsch Defence. Black looks to weaken his king side pawn structure in exchange for some deadly attacks against white.
White must play carefully as there are many traps and dangerous lines that black can play.

Smerdon's Scandinavian Defence

The link to the PGN and slide presentation are available below.
http://www.mediafire.com/view/gyw0ai9wl8z52rd/50_Moves_Magazine_%E2%80%93_Webinar.pdf
http://www.mediafire.com/download/hzbx5mg5ot2mx0g/50_Moves_Magazine_-_Webinar.pgnDavid's excellent book on the Portuguese is available on his site at http://www.davidsmerdon.com/

20/20 Chess Calculation

Chess video looking at the process of calculation and how it can be utilised to evaluate positions in a chess game.
1. We ascertain candidate moves from the mo...

Chess video looking at the process of calculation and how it can be utilised to evaluate positions in a chess game.
1. We ascertain candidate moves from the most forcing moves in the position,
a) checks (the most forcing)
b) captures
c) attacking moves
2. we visualise the sequence of events and split this sequence into episodes if its very long
3. We evaluate the position after the sequence of events and ask ourselves important question
a) does it increase or decrease the mobility of my chess pieces?
does it increase or decrease the mobility of my opponents chess pieces
b) does my opponent have any forcing moves (checks, captures, attacking moves) after the sequence of events has transpired? If so, are they dangerous?
thus calculation is used to subject chess moves to falsification to ascertain whether they are solid or otherwise and the hope is that we will not only avoid making blunders but that our moves will be sound and fully verified by the process of calculation. This is an important skill to try to master because there will not always be positional features on the chess board which we can utilise and much of the combat in a chess game is hand to hand anyway.
Many thanks for taking the time MSK Chess :D
https://www.facebook.com/msk.chess
chess, echecs , ajedrez , xadrez , shahmati , szachy , sjakk , schach , schaken , satranj , шахматы , شطرنج

Chess video looking at the process of calculation and how it can be utilised to evaluate positions in a chess game.
1. We ascertain candidate moves from the most forcing moves in the position,
a) checks (the most forcing)
b) captures
c) attacking moves
2. we visualise the sequence of events and split this sequence into episodes if its very long
3. We evaluate the position after the sequence of events and ask ourselves important question
a) does it increase or decrease the mobility of my chess pieces?
does it increase or decrease the mobility of my opponents chess pieces
b) does my opponent have any forcing moves (checks, captures, attacking moves) after the sequence of events has transpired? If so, are they dangerous?
thus calculation is used to subject chess moves to falsification to ascertain whether they are solid or otherwise and the hope is that we will not only avoid making blunders but that our moves will be sound and fully verified by the process of calculation. This is an important skill to try to master because there will not always be positional features on the chess board which we can utilise and much of the combat in a chess game is hand to hand anyway.
Many thanks for taking the time MSK Chess :D
https://www.facebook.com/msk.chess
chess, echecs , ajedrez , xadrez , shahmati , szachy , sjakk , schach , schaken , satranj , шахматы , شطرنج

Famous Chess Game: Kasparov vs Topalov 1999 (Kasparov's Immortal)

In what is arguably the greatest chess match ever played, Kasparov shows why he is considered to be the best chess player of all time in his "Immortal" game. T...

In what is arguably the greatest chess match ever played, Kasparov shows why he is considered to be the best chess player of all time in his "Immortal" game. There are so many amazing moves I lost count. Hopefully you learn as much from the game as I did studying it.
http://www.thechesswebsite.com
Chess Software used in the video can be found at http://www.chesscentral.com and http://www.chessok.com

In what is arguably the greatest chess match ever played, Kasparov shows why he is considered to be the best chess player of all time in his "Immortal" game. There are so many amazing moves I lost count. Hopefully you learn as much from the game as I did studying it.
http://www.thechesswebsite.com
Chess Software used in the video can be found at http://www.chesscentral.com and http://www.chessok.com

" 👑 Magnus Carlsen vs Eric Hansen 🔥 Chess.com Matchup October 26, 2017"
https://www.youtube.com/watch?v=tT8yqWVfauE --~--
Subscribe: https://www.youtube.com/subscribe_widget?p=KchessK
♚ Playlists: https://www.youtube.com/channel/UCVaQOn6bNwDTwWTbEbxt3Zw/playlists
December 2012
Discussion led by: Ksenja Horvat Petrovčič. Garry Kasparov, the 13thWorld Chess Champion and one of the most recognizable faces of the Russian opposition, has come as a guest of MariborEuropean Capital of Culture. In the interview, which was filmed exclusively for TelevisionSlovenia, Kasparov talked about the political situation in Russia, Vishy Anand, the twenty-year period when he led the global chess game, the orientations of modern chess and chess academy, which was opened in Slovenia, women and chess, and more. This is a very well conducted interview by the host and one of the best interviews I have seen in recent years. I am sure you will enjoy the video.
♚
Garry Kimovich Kasparov (Russian: Га́рри Ки́мович Каспа́ров, Russian pronunciation: [ˈɡarʲɪ ˈkʲiməvʲɪtɕ kɐˈsparəf]; born Garik Kimovich Weinstein, 13 April 1963) is a Russian (formerly Soviet) chess Grandmaster, former World Chess Champion, writer, and political activist, considered by many to be the greatest chess player of all time. From 1986 until his retirement in 2005, Kasparov was ranked world No. 1 for 225 out of 228 months. His peak rating of 2851, achieved in 1999, was the highest recorded until 2013. Kasparov also holds records for consecutive professional tournament victories (15) and Chess Oscars (11). Kasparov became the youngest ever undisputed World Chess Champion in 1985 at age 22 by defeating then-champion Anatoly Karpov. He held the official FIDE world title until 1993, when a dispute with FIDE led him to set up a rival organization, the Professional Chess Association. In 1997 he became the first world champion to lose a match to a computer under standard time controls, when he lost to the IBM supercomputer Deep Blue in a highly publicized match. He continued to hold the "Classical" World Chess Championship until his defeat by Vladimir Kramnik in 2000. Kasparov announced his retirement from professional chess on 10 March 2005, so that he could devote his time to politics and writing. He formed the United Civil Front movement, and joined as a member of The Other Russia, a coalition opposing the administration and policies of Vladimir Putin. In 2008, he announced an intention to run as a candidate in the 2008 Russian presidential race, but failure to find a sufficiently large rental space to assemble the number of supporters that is legally required to endorse such a candidacy led him to withdraw. Kasparov blamed "official obstruction" for the lack of available space. Although he is widely regarded in the West as a symbol of opposition to Putin, support for him as a candidate was very low. The political climate in Russia reportedly makes it difficult for opposition candidates to organize. He is currently on the board of directors for the Human Rights Foundation and chairs its International Council. Kasparov was born Garik Kimovich Weinstein (Russian: Гарик Вайнштейн) in Baku, AzerbaijanSSR (now Azerbaijan), Soviet Union. His father, Kim Moiseyevich Weinstein, was Russian Jewish, and his mother, Klara Gasparian, was Armenian.Kasparov has described himself as a "self-appointed Christian", although "very indifferent". Kasparov first began the serious study of chess after he came across a chess problem set up by his parents and proposed a solution. His father died of leukemia when Garry was seven years old. At the age of twelve, Garry adopted his mother's Armenian surname, Gasparyan, modifying it to a more Russified version, Kasparov. From age 7, Kasparov attended the Young Pioneer Palace in Baku and, at 10 began training at Mikhail Botvinnik's chess school under noted coach Vladimir Makogonov. Makogonov helped develop Kasparov's positional skills and taught him to play the Caro-Kann Defence and the TartakowerSystem of the Queen's Gambit Declined. Kasparov won the Soviet Junior Championship in Tbilisi in 1976, scoring 7 points of 9, at age 13. He repeated the feat the following year, winning with a score of 8½ of 9. Read more: http://en.wikipedia.org/wiki/Garry_Kasparov

" 👑 Magnus Carlsen vs Eric Hansen 🔥 Chess.com Matchup October 26, 2017"
https://www.youtube.com/watch?v=tT8yqWVfauE --~--
Subscribe: https://www.youtube.com/subscribe_widget?p=KchessK
♚ Playlists: https://www.youtube.com/channel/UCVaQOn6bNwDTwWTbEbxt3Zw/playlists
December 2012
Discussion led by: Ksenja Horvat Petrovčič. Garry Kasparov, the 13thWorld Chess Champion and one of the most recognizable faces of the Russian opposition, has come as a guest of MariborEuropean Capital of Culture. In the interview, which was filmed exclusively for TelevisionSlovenia, Kasparov talked about the political situation in Russia, Vishy Anand, the twenty-year period when he led the global chess game, the orientations of modern chess and chess academy, which was opened in Slovenia, women and chess, and more. This is a very well conducted interview by the host and one of the best interviews I have seen in recent years. I am sure you will enjoy the video.
♚
Garry Kimovich Kasparov (Russian: Га́рри Ки́мович Каспа́ров, Russian pronunciation: [ˈɡarʲɪ ˈkʲiməvʲɪtɕ kɐˈsparəf]; born Garik Kimovich Weinstein, 13 April 1963) is a Russian (formerly Soviet) chess Grandmaster, former World Chess Champion, writer, and political activist, considered by many to be the greatest chess player of all time. From 1986 until his retirement in 2005, Kasparov was ranked world No. 1 for 225 out of 228 months. His peak rating of 2851, achieved in 1999, was the highest recorded until 2013. Kasparov also holds records for consecutive professional tournament victories (15) and Chess Oscars (11). Kasparov became the youngest ever undisputed World Chess Champion in 1985 at age 22 by defeating then-champion Anatoly Karpov. He held the official FIDE world title until 1993, when a dispute with FIDE led him to set up a rival organization, the Professional Chess Association. In 1997 he became the first world champion to lose a match to a computer under standard time controls, when he lost to the IBM supercomputer Deep Blue in a highly publicized match. He continued to hold the "Classical" World Chess Championship until his defeat by Vladimir Kramnik in 2000. Kasparov announced his retirement from professional chess on 10 March 2005, so that he could devote his time to politics and writing. He formed the United Civil Front movement, and joined as a member of The Other Russia, a coalition opposing the administration and policies of Vladimir Putin. In 2008, he announced an intention to run as a candidate in the 2008 Russian presidential race, but failure to find a sufficiently large rental space to assemble the number of supporters that is legally required to endorse such a candidacy led him to withdraw. Kasparov blamed "official obstruction" for the lack of available space. Although he is widely regarded in the West as a symbol of opposition to Putin, support for him as a candidate was very low. The political climate in Russia reportedly makes it difficult for opposition candidates to organize. He is currently on the board of directors for the Human Rights Foundation and chairs its International Council. Kasparov was born Garik Kimovich Weinstein (Russian: Гарик Вайнштейн) in Baku, AzerbaijanSSR (now Azerbaijan), Soviet Union. His father, Kim Moiseyevich Weinstein, was Russian Jewish, and his mother, Klara Gasparian, was Armenian.Kasparov has described himself as a "self-appointed Christian", although "very indifferent". Kasparov first began the serious study of chess after he came across a chess problem set up by his parents and proposed a solution. His father died of leukemia when Garry was seven years old. At the age of twelve, Garry adopted his mother's Armenian surname, Gasparyan, modifying it to a more Russified version, Kasparov. From age 7, Kasparov attended the Young Pioneer Palace in Baku and, at 10 began training at Mikhail Botvinnik's chess school under noted coach Vladimir Makogonov. Makogonov helped develop Kasparov's positional skills and taught him to play the Caro-Kann Defence and the TartakowerSystem of the Queen's Gambit Declined. Kasparov won the Soviet Junior Championship in Tbilisi in 1976, scoring 7 points of 9, at age 13. He repeated the feat the following year, winning with a score of 8½ of 9. Read more: http://en.wikipedia.org/wiki/Garry_Kasparov

The ColoradoGambit is an aggressive line in the Nimzowitsch Defence. Black looks to weaken his king side pawn structure in exchange for some deadly attacks against white.
White must play carefully as there are many traps and dangerous lines that black can play.

The ColoradoGambit is an aggressive line in the Nimzowitsch Defence. Black looks to weaken his king side pawn structure in exchange for some deadly attacks against white.
White must play carefully as there are many traps and dangerous lines that black can play.

Smerdon's Scandinavian Defence

The link to the PGN and slide presentation are available below.
http://www.mediafire.com/view/gyw0ai9wl8z52rd/50_Moves_Magazine_%E2%80%93_Webinar.pdf
http://w...

The link to the PGN and slide presentation are available below.
http://www.mediafire.com/view/gyw0ai9wl8z52rd/50_Moves_Magazine_%E2%80%93_Webinar.pdf
http://www.mediafire.com/download/hzbx5mg5ot2mx0g/50_Moves_Magazine_-_Webinar.pgnDavid's excellent book on the Portuguese is available on his site at http://www.davidsmerdon.com/

The link to the PGN and slide presentation are available below.
http://www.mediafire.com/view/gyw0ai9wl8z52rd/50_Moves_Magazine_%E2%80%93_Webinar.pdf
http://www.mediafire.com/download/hzbx5mg5ot2mx0g/50_Moves_Magazine_-_Webinar.pgnDavid's excellent book on the Portuguese is available on his site at http://www.davidsmerdon.com/

Tutorial #15: How to Print a Game to PDF in Chess King

Chess KingVideo software Tutorial #15 by Steve Lopez. You will learn how to print your game to PDF with diagrams and variations. Learn how to add a diagram to your notation. Chess King with Houdini 2 is an affordable and powerful chess software. With Chess King you can play chess, solve puzzles, analyze your games with the strongest engine available and have access to more than the 5 million game GigaKing database. At the moment there is a Coupon Code INTROKING50 that will let you save $50 from the list $99 price of Chess King on chess - king dot com (chess dash king dot com).

1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinfor...
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns
Research paper: "Mastering Chess and Shogi by Self-Play with a
GeneralReinforcement LearningAlgorithm" :
David Silver,1∗ ThomasHubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1
https://arxiv.org/pdf/1712.01815.pdf
The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ....
Read more at: https://arxiv.org/pdf/1712.01815.pdf
What is reinforcement learning?
https://en.wikipedia.org/wiki/Reinforcement_learning
"Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs."
What is this company called Deepmind ?
https://en.wikipedia.org/wiki/DeepMind
DeepMind TechnologiesLimited is a British artificial intelligence company founded in September 2010.
Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]
The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.
♚Play at: http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher's "Crushing the King" video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
http://goo.gl/7HJcDq
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Follow me at Google+ : http://www.google.com/+kingscrusher ►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher

20/20 Chess Calculation

Chess video looking at the process of calculation and how it can be utilised to evaluate positions in a chess game.
1. We ascertain candidate moves from the most forcing moves in the position,
a) checks (the most forcing)
b) captures
c) attacking moves
2. we visualise the sequence of events and split this sequence into episodes if its very long
3. We evaluate the position after the sequence of events and ask ourselves important question
a) does it increase or decrease the mobility of my chess pieces?
does it increase or decrease the mobility of my opponents chess pieces
b) does my opponent have any forcing moves (checks, captures, attacking moves) after the sequence of events has transpired? If so, are they dangerous?
thus calculation is used to subject chess moves to falsification to ascertain whether they are solid or otherwise and the hope is that we will not only avoid making blunders but that our moves will be sound and fully verified by the process of calculation. This is an important skill to try to master because there will not always be positional features on the chess board which we can utilise and much of the combat in a chess game is hand to hand anyway.
Many thanks for taking the time MSK Chess :D
https://www.facebook.com/msk.chess
chess, echecs , ajedrez , xadrez , shahmati , szachy , sjakk , schach , schaken , satranj , шахматы , شطرنج

57:13

Play the Panov-Botvinnik Attack | Chess Openings Explained

Caleb Denby explores the Panov-Botvinnik Attack, an exchange variation of the Caro-Kann De...

Famous Chess Game: Kasparov vs Topalov 1999 (Kasparov's Immortal)

In what is arguably the greatest chess match ever played, Kasparov shows why he is considered to be the best chess player of all time in his "Immortal" game. There are so many amazing moves I lost count. Hopefully you learn as much from the game as I did studying it.
http://www.thechesswebsite.com
Chess Software used in the video can be found at http://www.chesscentral.com and http://www.chessok.com

55:10

Crush the Caro-Kann - Chess Openings Explained

Jonathan Schrantz shows how to play the classical line of the Caro-Kann Defence. See thre...

♚ How Life Imitates Chess-Interview With Garry Kasparov

" 👑 Magnus Carlsen vs Eric Hansen 🔥 Chess.com Matchup October 26, 2017"
https://www.youtube.com/watch?v=tT8yqWVfauE --~--
Subscribe: https://www.youtube.com/subscribe_widget?p=KchessK
♚ Playlists: https://www.youtube.com/channel/UCVaQOn6bNwDTwWTbEbxt3Zw/playlists
December 2012
Discussion led by: Ksenja Horvat Petrovčič. Garry Kasparov, the 13thWorld Chess Champion and one of the most recognizable faces of the Russian opposition, has come as a guest of MariborEuropean Capital of Culture. In the interview, which was filmed exclusively for TelevisionSlovenia, Kasparov talked about the political situation in Russia, Vishy Anand, the twenty-year period when he led the global chess game, the orientations of modern chess and chess academy, which was opened in Slovenia, women and chess, and more. This is a very well conducted interview by the host and one of the best interviews I have seen in recent years. I am sure you will enjoy the video.
♚
Garry Kimovich Kasparov (Russian: Га́рри Ки́мович Каспа́ров, Russian pronunciation: [ˈɡarʲɪ ˈkʲiməvʲɪtɕ kɐˈsparəf]; born Garik Kimovich Weinstein, 13 April 1963) is a Russian (formerly Soviet) chess Grandmaster, former World Chess Champion, writer, and political activist, considered by many to be the greatest chess player of all time. From 1986 until his retirement in 2005, Kasparov was ranked world No. 1 for 225 out of 228 months. His peak rating of 2851, achieved in 1999, was the highest recorded until 2013. Kasparov also holds records for consecutive professional tournament victories (15) and Chess Oscars (11). Kasparov became the youngest ever undisputed World Chess Champion in 1985 at age 22 by defeating then-champion Anatoly Karpov. He held the official FIDE world title until 1993, when a dispute with FIDE led him to set up a rival organization, the Professional Chess Association. In 1997 he became the first world champion to lose a match to a computer under standard time controls, when he lost to the IBM supercomputer Deep Blue in a highly publicized match. He continued to hold the "Classical" World Chess Championship until his defeat by Vladimir Kramnik in 2000. Kasparov announced his retirement from professional chess on 10 March 2005, so that he could devote his time to politics and writing. He formed the United Civil Front movement, and joined as a member of The Other Russia, a coalition opposing the administration and policies of Vladimir Putin. In 2008, he announced an intention to run as a candidate in the 2008 Russian presidential race, but failure to find a sufficiently large rental space to assemble the number of supporters that is legally required to endorse such a candidacy led him to withdraw. Kasparov blamed "official obstruction" for the lack of available space. Although he is widely regarded in the West as a symbol of opposition to Putin, support for him as a candidate was very low. The political climate in Russia reportedly makes it difficult for opposition candidates to organize. He is currently on the board of directors for the Human Rights Foundation and chairs its International Council. Kasparov was born Garik Kimovich Weinstein (Russian: Гарик Вайнштейн) in Baku, AzerbaijanSSR (now Azerbaijan), Soviet Union. His father, Kim Moiseyevich Weinstein, was Russian Jewish, and his mother, Klara Gasparian, was Armenian.Kasparov has described himself as a "self-appointed Christian", although "very indifferent". Kasparov first began the serious study of chess after he came across a chess problem set up by his parents and proposed a solution. His father died of leukemia when Garry was seven years old. At the age of twelve, Garry adopted his mother's Armenian surname, Gasparyan, modifying it to a more Russified version, Kasparov. From age 7, Kasparov attended the Young Pioneer Palace in Baku and, at 10 began training at Mikhail Botvinnik's chess school under noted coach Vladimir Makogonov. Makogonov helped develop Kasparov's positional skills and taught him to play the Caro-Kann Defence and the TartakowerSystem of the Queen's Gambit Declined. Kasparov won the Soviet Junior Championship in Tbilisi in 1976, scoring 7 points of 9, at age 13. He repeated the feat the following year, winning with a score of 8½ of 9. Read more: http://en.wikipedia.org/wiki/Garry_Kasparov

Smerdon's Scandinavian Defence...

Top 10 Most Popular Responses to 1. d4 | Chess Ope...

She's Tough

Well, I was lookin' for just a little bit moreThan just another pretty faceSomeone who would be strong and trueIf trouble ever came my wayToo many times, I've been left behindWhen my back was up against the wallBut now I found someone who ain't gonna runNo matter how the dice may fallShe's tough enough to stand by meWhen it all comes tumblin' downShe's tough enough to steady meWhen I'm walkin' on shaky groundWhen it comes to tendernessBuddy, that's when she's at her bestWhen the goin' gets rough, she's toughWell, she's soft and sweet and she's good to meEven when times are badShe picks me up with a gentle touchAnytime I'm feelin' sadDon't you know I'll never let her go'Cause she makes it so plain to seeWhen love is real, it's strong as steelAnd hers is all I'll ever needShe's tough enough to stand by meWhen it all comes tumblin' downShe's tough enough to steady meWhen I'm walkin' on shaky groundWhen it comes to tendernessBuddy, that's when she's at her bestWhen the goin' gets rough, she's toughAnd I'm so glad that I'm her man'Cause when the you know what starts to hit the fanShe's tough enough to stand by meWhen it all comes tumblin' downShe's tough enough to steady meWhen I'm walkin' on shaky groundWhen it comes to tendernessBuddy, that's when she's at her bestWhen the goin' gets roughShe's tough enough to stand by meWhen it all comes tumblin' downShe's tough enough to steady meWhen I'm walkin' on shaky groundWhen it comes to tendernessBuddy, that's when she's at her bestWhen the goin' gets roughShe's tough enough to stand by me

LONDON (AP) — A British surgeon has admitted assaulting two patients by burning his initials into their livers during transplant operations ...Bramhall used an argon beam coagulator, which seals bleeding blood vessels with an electric beam, to mark his initials on the organs ... ....

District JudgeTed Stewart said during a hearing in Salt Lake City that Lyle Jeffs deserved the 57-month prison sentence because his behavior showed he doesn't respect U.S ... Jeffs is an adult. He knows right from wrong." ... He was ordered to pay $1 million in restitution ... "I do humbly accept my responsibly for my actions ... The FBI put up a $50,000 reward....

Janet Yellen announced that for the third time this year and the fifth time since the financial crisis, the Federal Reserve was increasing interest rates another quarter of a point on Wednesday, according to National Public Radio. Federal policymakers aid the increase in the benchmark federal funds rate would shift from 1.25 percent to 1.5 percent, the third increase on the key rate this year ...Economic growth in the U.S....

search tools

You can search using any combination of the items listed below.

Good effort but the games were seemingly rigged Analysis DeepMind claimed this month its latest AI system – AlphaZero – mastered chess and Shogi as well as Go to "superhuman levels" within a handful of hours ... ....

Dress up (in drag) for holiday BINGO...KBUTDragBingo will start at 8 p.m. and players are encouraged to dress as the opposite sex and take a ride on the wild side ... In addition to the regular games, we’ll celebrate the season with cookie decorating, cocoa and comedy! We’ll decorate cookies and drink cocoa while we play games, such as Apples to Apples, Harry PotterChess, Candy Land, King ofTokyo, and others. Then at 7.30 p.m ... Birthdays.....

She is very much the queen in India’s domestic chess; she won the National women’s premier championship for the fourth successive year at Surat a few days ago ... As a young girl, it was the many triumphs of Emlee — now an engineer in Germany — as a schoolgirl that made Padmini competitive as a chess player ... “When I began playing chess — because my ......

KARACHI. Renowned journalist Nargis Khanum died of a heart attack on Tuesday afternoon. She was coming to the Karachi Press Club (KPC) in her car when she felt uncomfortable ... to have lunch and to play chess ... For the last few years, distinguished media person and social commentator Ghazi Salahuddin had been her chess partner. Both had kept their Tuesday and Friday afternoons for the club where they would solely come to play chess ... ....